Ethnicity

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Major | Gender | Career | Ethnicity | First Generation Students | Dependents | Medical Condition | Medical Care | Analysis

Function

To begin, let’s explore the function we created to run all of this statistical analysis:

mysubsetMDS <- function(x){
  mysubset <- df %>% 
    select(starts_with(x))
  
  meta <- metaMDS(mysubset)
  MDS_df <- data.frame(MDS1=meta$points[,1],MDS2=meta$points[,2]) %>%
    cbind(demo)
  return(MDS_df)
}

In English, this function allows us to run the MDS according to each subset of demographic and each subset of question type that we want. Obviously, in this page, we are exploring Ethnicity.

Question Types

Science Identity

Let’s explore the science identity subset of questions first. Running our function we created and plotting it, we are left with this image of the plot:

This is great and all, but let’s run an adonis test to see if there is a significant difference in how different ethnicity goals responded to science identity questions:

## 
## Call:
## adonis(formula = si ~ demo$ethnicity) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                 Df SumsOfSqs  MeanSqs F.Model     R2 Pr(>F)
## demo$ethnicity   9    0.3197 0.035524  1.1163 0.0322  0.352
## Residuals      302    9.6102 0.031822         0.9678       
## Total          311    9.9299                  1.0000

This shows that the differences in answers are not significant, according to ethnicity.

Career Motivation

Next, let’s look into Carer Motivation:

And an Adonis test:

## 
## Call:
## adonis(formula = cm ~ demo$ethnicity) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                 Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)    
## demo$ethnicity   9    1.9146 0.212738   4.616 0.12093  0.001 ***
## Residuals      302   13.9182 0.046087         0.87907           
## Total          311   15.8329                  1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Look at that! The differences are significant for Career Motivation across ethnicity.

Intrinsic Motivation

Now, Intrinsic Motivation:

And an Adonis test:

## 
## Call:
## adonis(formula = im ~ demo$ethnicity) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                 Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)    
## demo$ethnicity   9    1.0516 0.116849  4.5473 0.11934  0.001 ***
## Residuals      302    7.7602 0.025696         0.88066           
## Total          311    8.8119                  1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Look at that! The differences are significant for Intrinsic Motivation across ethnicity.

Self-Determination

Now, Self-Determination:

And an Adonis test:

## 
## Call:
## adonis(formula = sd ~ demo$ethnicity) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                 Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)    
## demo$ethnicity   9    0.9636 0.107065  4.8598 0.12651  0.001 ***
## Residuals      302    6.6533 0.022031         0.87349           
## Total          311    7.6168                  1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Look at that! The differences are significant for Self-Determination across ethnicity.

Self-Efficacy

Now, Self-Efficacy:

And an Adonis test:

## 
## Call:
## adonis(formula = se ~ demo$ethnicity) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                 Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)   
## demo$ethnicity   9    0.8957 0.099526  3.9496 0.10531  0.002 **
## Residuals      302    7.6102 0.025199         0.89469          
## Total          311    8.5059                  1.00000          
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Look at that! The differences are significant for Self-Efficacy across ethnicity.

Grade Motivation

Now, Grade Motivation:

And an Adonis test:

## 
## Call:
## adonis(formula = gm ~ demo$ethnicity) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                 Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)    
## demo$ethnicity   9    1.2175 0.135278   7.201 0.17668  0.001 ***
## Residuals      302    5.6734 0.018786         0.82332           
## Total          311    6.8909                  1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Look at that! The differences are significant for Grade Motivation across ethnicity.

Competency in Science

Now, Competency in Science:

And an Adonis test:

## 
## Call:
## adonis(formula = sci_comp ~ demo$ethnicity) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                 Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$ethnicity   9     0.366 0.040663  1.1765 0.03387   0.28
## Residuals      302    10.438 0.034563         0.96613       
## Total          311    10.804                  1.00000

This shows that the differences in answers are not significant, according to ethnicity.

Personal Community Orientation

Now, Personal Community Orientation:

And an Adonis test:

## 
## Call:
## adonis(formula = per_comm_orient ~ demo$ethnicity) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                 Df SumsOfSqs   MeanSqs F.Model     R2 Pr(>F)  
## demo$ethnicity   9   0.25634 0.0284827   3.457 0.0934  0.038 *
## Residuals      302   2.48824 0.0082392         0.9066         
## Total          311   2.74458                   1.0000         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Look at that! The differences are significant for Personal Communal Orientation across ethnicity.

Science Community Orientation

Now, Competency in Science:

Well crap, something is broke there. Let’s try running a different form of MDS, giving us a similar analysis in a different way. We can interpret this data similarly to how we did before:

And an Adonis test:

## 
## Call:
## adonis(formula = sci_comm_orient ~ demo$ethnicity) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                 Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)  
## demo$ethnicity   9    0.2587 0.028741  2.0026 0.05632  0.075 .
## Residuals      302    4.3343 0.014352         0.94368         
## Total          311    4.5929                  1.00000         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

This shows that the differences in answers are not significant, according to major.

Conclusion

Ultimately, what we can understand from all of this is that students significantly answer Career Motivation, Intrinsic Motivation, Self-Determination, Self-Efficacy, Grade Motivation, and Personal Communal Orientation questions differently according to their ethnicity.

In summary, let’s take a look at the running list we are making of which questions were significant and not according to demographics:

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